System and method for selecting a respirator

ABSTRACT

Apparatus and associated methods may relate to a system for predicting a respirator fit by comparing a respirator model in a deformed state to a specific facial model. In an illustrative example, an internal measurement may be calculated between an inside part of the respirator model and the facial model. The internal measurement may be compared against a predetermined threshold to determine a fit of the respirator model, for example. In various implementations, the internal measurement may be a distance and/or a volume between the respirator and facial model. In some implementations, a 3D representation of the respirator model may be displayed upon a 3D representation of the facial model. In some implementations, a color-coded facial display may characterize areas of comfort and discomfort with respect to the respirator model. For example, areas of comfort and discomfort may be objectively determined in view of an applied pressure by the respirator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/839,186, filed by Wang, et al., on Mar. 15, 2013, entitled “Systemand Method for Selecting a Respirator.” The entire contents of theforegoing application is incorporated herein by reference.

TECHNICAL FIELD

Various embodiments relate generally to personal protective equipmentand more specifically to a system and method for predicting an optimalfit of a respirator to a facial area.

BACKGROUND

Personal protective equipment (PPE), such as for example respirators,are widely used in a variety of different applications. For example,many workplaces that subject an employee to hazardous atmosphericconditions require the employee to wear respiratory protection forseveral hours per day. To be effective, respiratory protection requiresa proper seal upon a facial area of the user. A poor seal and thus poorfit may result in leakage and the possibility of the inhalation ofcontaminants.

Finding a respirator that fits a unique facial area of the user canrequire the user to try on many different types and sizes ofrespirators. In some workplace environments, valuable time can be spentattempting to find an optimal fitting respirator. In other workplaceenvironments, an employee may not be able to find a respirator having asuitable fit. For example, the employee may not be given adequate timeto try on different respirators, or the employee may not be given anadequate variety of respirator samples to try. Because of the generallack of efficiency and practicality in the prior art there may be a needfor a new and improved system and method for predicting an optimal fitof a respirator to a facial area.

SUMMARY

Apparatus and associated methods may relate to a system for predicting arespirator fit by comparing a respirator model in a deformed state to aspecific facial model. In an illustrative example, an internalmeasurement may be calculated between an inside part of the respiratormodel and the facial model. The internal measurement may be comparedagainst a predetermined threshold to determine a fit of the respiratormodel, for example. In various implementations, the internal measurementmay be a distance and/or a volume between the respirator and facialmodel. In some implementations, a 3D representation of the respiratormodel may be displayed upon a 3D representation of the facial model. Insome implementations, a color-coded facial display may characterizeareas of comfort and discomfort with respect to the respirator model.For example, areas of comfort and discomfort may be objectivelydetermined in view of an applied pressure by the respirator.

In accordance with an exemplary embodiment, an image capture device maygenerate point cloud data of a body part model and a PPE model. Forexample, an image capture device may generate point cloud data of afacial area and a respirator. In an exemplary embodiment, point clouddata may be used to overlay a respirator model on a facial model todetermine a virtual placement of the respirator model on the facialmodel. For example, a rigid registration method may be used to alignpoint clouds of the facial model and the respirator model. In someimplementations, identifying feature points of the body part model(e.g., nose, mouth) may be correlated with the generated point cloud. Insome implementations, a contact line may be determined upon the facialarea. Determination of the contact line may provide identification of aportion of a facial area that aligns with the inside part of therespirator.

Various embodiments may achieve one or more advantages. For example,some embodiments may objectively determine whether the hidden, insidepart of the respirator will make contact with a portion of the facialarea. In accordance with an exemplary embodiment, a predetermineddeformation parameter of an outside part (e.g., outside surface) of therespirator may be attributed to a corresponding inside part (e.g.,inside surface) of the respirator. The deformation parameter may bedetermined at each of the vertices or point cloud of the outside andinside parts of the respirator. In an exemplary embodiment, internalmeasurements may be made between each deformed point cloud or each ofthe vertices and an aligned part of the facial area to determine arespirator fit.

The details of various embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an overview of an exemplary respirator selection system.

FIG. 2 depicts a flowchart of an exemplary PPE selection system.

FIG. 3 depicts an overview of an exemplary PPE selection system.

FIG. 4 depicts a flowchart of an exemplary process of a static modelingmodule.

FIG. 5A depicts a flowchart of an exemplary dynamic modeling moduleusing actual body movement.

FIG. 5B depicts an exemplary graphical view of the dynamic modelingmodule of FIG. 5A.

FIG. 6A depicts a flowchart of an exemplary dynamic modeling moduleusing simulated body movement.

FIG. 6B depicts a graphical view of the exemplary dynamic modelingmodule of FIG. 6A.

FIG. 7 depicts an overview of another exemplary PPE selection system.

FIG. 8 depicts a flowchart of an exemplary PPE selection system.

FIG. 8 depicts a flowchart of an exemplary PPE selection system.

FIG. 9A depicts a flowchart of another exemplary PPE selection system.

FIG. 9B depicts an exemplary center part on a ROI as defined withreference to FIG. 9A.

FIG. 10 depicts a flowchart of an exemplary deformation process.

FIG. 11 depicts a graphical representation of an exemplary color-codeddisplay of a PPE fit.

FIG. 12 depicts a flowchart of an exemplary color-coded resultgenerator.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

To aid understanding, this document is organized as follows. First, anexemplary PPE selection system for comparing a specific user body partwith a respective type of PPE and providing a recommendation and outputto a user detailing a fit of the PPE upon the body part is brieflyintroduced with reference to FIGS. 1-2. Then, with reference to FIGS.3-6B, the discussion turns to detail an exemplary PPE selection systemadapted to evaluate a fit of PPE upon a user based on dynamicconditions. Specifically, FIG. 3 illustrates an exemplary overview ofthe PPE selection system as detailed in FIGS. 4-6B. FIG. 4 illustratesan exemplary method of obtaining a static model of the user. FIGS. 5 a-5b detail an exemplary method of having the user perform actual bodymovements to obtain a dynamic model, and FIGS. 6 a-6 b detail anexemplary method of simulating body movements to obtain a dynamic model.Next, FIGS. 7-10 illustrate another exemplary PPE selection systemadapted to evaluate a fit of PPE upon a user in both based on aninternal space between the PPE and the user body part. Finally, withreference to FIGS. 11-12, an exemplary fit recommendation is illustratedthat portrays a fit experience of the PPE upon the user based oncolor-coding.

FIG. 1 depicts an overview of an exemplary respirator selection system.A system 100 for selecting a PPE is illustrated, where the system 100may provide recommendations to a user on which PPE will provide anoptimal fit, or provide a best fit among available PPE. A fit level ofthe PPE upon the user may be determined by a variety of factorsdetermined by the user, employee, and/or manufacturer. For example, anamount of predicted leakage of ambient air through a sealing edge of therespirator may be a determined factor for determining a fit level of therespirator on a facial area of the user. In an exemplary embodiment, ifa respirator were to permit a leakage at a rate beyond a predeterminedthreshold, the respective respirator may be given a low score and/or anon recommendation. In another exemplary embodiment, a force applied bythe respirator upon a facial area of the user may be a determinantfactor on a recommendation of a particular respirator type of size. Ifthe respirator is predicted to apply pressure to the facial area at arate or force exceeding a threshold, the respirator may be given a lowscore and/or a non recommendation because of a possible low comfortlevel provided to the user by the respirator, for example.

The system 100 may provide fit recommendations or scores based uponcaptured and analyzed images of the user body part (e.g., facial area)and PPE (e.g., respirator). In the depicted example, the system 100include one or more image capture devices 105 for capturingrepresentations of a user body part 110 and/or a type of PPE 115. In thedepicted example, the user body part 110 is a user facial area. The PPE115 may be a respirator, for example. In an exemplary embodiment, aseries of two-dimensional (2D) images may be captured by the imagecapture device 105 from which a three-dimensional (3D) image may beassembled. In other exemplary embodiments, a 2D image may be used todetermine a PPE fit. In other exemplary embodiments, the image capturedevice may capture a 3D representation of the body part 110 and/or thePPE 115. In some examples, facial coordinate data representative of ashape of a facial area and respirator coordinate data representative ofa respirator shape may be analyzed to provide a fit recommendation. Inan exemplary embodiment, the system 100 may load previously capturedand/or generated body parts 110 and/or PPE 115.

The system 100 may be used for selecting a variety of PPE 115 to be wornon the intended body part 110. For example, in certain embodiments thesystem 100 may predictively choose an optimal fitting glove to fit auser hand. In other exemplary embodiments, the system may choose anoptimally fitting helmet to fit a head of a user. In an exemplaryembodiment, several respirator point cloud data sets each indicative ofa specific size and shape respirator 115 may be stored in a database120. For example, each respirator that an employer offers to employeesmay be analyzed with associated representative point cloud data, wherethe representative point cloud data may be stored in a database 120. Inan exemplary embodiment, the point cloud data may include x, y, zcoordinates which may be assembled to form a 3D image of the intendedPPE 115 and/or user body part 110. In an exemplary embodiment, thedatabase 120 may be accessible over a wide-area network (e.g., Internet)to permit a wide selection of PPE 115 to users without the need topersonally capture data representative of each eligible PPE 115.

A comparator module 125 compares the PPE 115 with the body part 110 todetermine whether the PPE 115 will properly fit the respective body part110. In an exemplary embodiment, the PPE 115 is overlaid upon the bodypart 110. For example, a point cloud and/or vertices may be alignedbetween the PPE 115 and the body part 110. In an exemplary embodiment,the comparator module 125 uses a set of predetermined rules from a ruleslibrary 130 to determine whether the PPE 115 properly fits the body part110. For example, the rules may require the sealing edge of a respiratornot to be in contact with the mouth of the user. In another exemplaryembodiment, the rules may require the respirator to have a surface areaas large as the respirator-receiving portion of the facial area of theuser. In another exemplary embodiment, the rules may identify a capturedbody part, such as for example a facial area, and direct the comparatormodule to only compare respirators from the database with the body part.In another exemplary embodiment, the rules may identify a captured bodypart, such as for example a hand, and direct the comparator module toonly compare gloves from the database with the body part and not tocompare respirators with the captured body part (e.g., hand).

After a fit of the evaluated PPE 115 and body part 110 has beendetermined, a simulator module 135 may display the fit. For example, thesimulator module 135 may display a representation of the respirator wornby the specific facial area of the user. In some examples, a predictedtightness or looseness of the PPE 115 relative the body part 110 may beemphasized in the simulator module 135. For example, a predicted leakagebetween the sealing edge of the respirator and the facial area may beemphasized. A report 140 may be outputted to the user to assist inproviding a recommendation on fit levels of each compared PPE 115. Insome examples, a list of evaluated PPE 115 may be included in the report140 with each of the evaluated PPE 115 having a score or fit levelassigned. In some examples, only recommended PPE 115 may be provided inthe report 140. In some examples, only the highest scoring three or fivePPE 115 may be provided in the report 140.

FIG. 2 depicts a flowchart of an exemplary PPE selection system. In anexemplary PPE selection system 200, a determination may be made ofwhether one or more PPE models fit a specific body part. In an exemplaryembodiment, the determination may be made by software operated on aprocessor module. For example, the software may determine whichrespirator of a plurality of respirator models from a respirator typedatabase fits a specific facial area of a user and output arecommendation to a user.

More specifically, data representing an exemplary body part may becaptured as in step 205. In an exemplary embodiment, the data may becaptured by an image capture device. For example, an image capturedevice may scan a body part to build a 3D representation of the bodypart. In another exemplary embodiment, data representing the body partmay be retrieved or computationally loaded. In an exemplary embodiment,the body part data may be retrieved from a body part database havingspecific body part shapes stored at an earlier date. In other exemplaryembodiments, the data representative of a body part may be a genericbody part computationally generated or morphed from one or more models.In an exemplary embodiment, the representative body part may be a facialarea of a user.

Additionally, data representing one or more types of PPE (e.g., helmets,gloves, PPE) may be captured as in step 210. In an exemplary embodiment,the data may be captured by an image capture device. For example, animage capture device may scan PPE to build a 3D representation of thePPE. In another exemplary embodiment, data representing the PPE may beretrieved or computationally loaded. In an exemplary embodiment, the PPEdata may be retrieved from a PPE database having specific PPE shapesstored at an earlier date. In an exemplary embodiment, therepresentative PPE may be a respirator.

A particular type of PPE (e.g., helmets, gloves, PPE) to be worn over orupon the loaded body part may be retrieved or computationally loaded asin step 215. For example, a PPE type including respirator models may beloaded if the intended body part may be a facial area. In anotherexemplary embodiment, a PPE type having gloves may be loaded if theintended body part may be a hand.

A first PPE model from the loaded relevant PPE type may be retrieved asin step 220 to be compared via a comparator module with the capturedbody part as in step 225. Each PPE model may be distinguishable becauseof a size, shape, or other criteria which may affect the fit of the PPEon the user body part. The comparison may determine whether the PPEmodel has a shape that will permit an acceptable fit over the shape ofthe body part. For example, the PPE model may be required to meet orexceed one or more thresholds or rules previously determined asindicative of proper or optimal fitting criteria. In some exemplaryembodiments, the PPE model may be required to fit the body part in bothstatic and dynamic states of the body part.

If the PPE model is determined to fit the body part as illustrated instep 230, the PPE model may be simulated on the body part as in step235. In an exemplary embodiment, the PPE model and body part may bedisplayed to the user in a 3D representation. In some exemplaryembodiments, the user may rotate and pan a 3D representation of thesimulated body part and PPE model. In some exemplary embodiments, thesimulated representation may provide visual feedback to the userdetailing areas of the PPE model that are not predicted or determined tofit the respective body part. For example, one or more colors may beoverlaid upon the representation to indicate areas upon the body partthat are predicted to be uncomfortable as a result of wearing the PPEmodel. In other examples, a blinking or flashing area may indicate anarea of the PPE model that does not conform to a minimum thresholddetermined to be required to provide a proper and/or comfortable fit.For example, a portion of a sealing edge of a respirator may blink if aleak is predicted to be present in the respective portion.

After the first PPE model is determined to fit and simulated to theuser, the software may determine if there are any other PPE models inthe chosen PPE type group that are to be evaluated against therespective body part as illustrated by step 240. If so, the softwarecycles to a second PPE model as illustrated by step 245 and repeats theprocess of steps 225-240. If there are no more PPE models from the PPEtype group, a report and proposal may be generated for output to theuser as illustrated in step 250. In some exemplary embodiments, thereport and proposal may include the top three PPE models that have thebest fit with respect to the specific body part. In some exemplaryembodiments, the top PPE models or all of the PPE models evaluated maybe provided with a fit score to the user. In some exemplary embodiments,a different PPE type group may require comparison with the body part, inwhich case some or all of the process may be repeated.

FIG. 3 depicts an overview of an exemplary PPE selection system. A PPEselection system 300 may be used to select an optimal fit PPE for a userbody part during dynamic conditions of the user body part. The PPEselection system 300 includes a device type selection module 305 forreceiving a command from a user 310. In an exemplary embodiment, thedevice type selection module 305 sends commands to a PPE database 315.The PPE database 315 may include a variety of types of PPE 320, such asfor example gloves, respirators, and helmets. In an exemplaryembodiment, the device type selection module 310 may relay a command 310indicative of a particular type of PPE 320, such as for example a facialrespirator. In an exemplary embodiment, the command 310 may beindicative of a particular user body part 325 to be matched with the PPE320 from the PPE database 315.

In some exemplary embodiments, the device type selection module 305 maydirect an image capture device (not shown) to capture a 2D or 3D imageof the selected body part 325. In some embodiments, the PPE 320 may bemodeled in a corresponding 3D shape. In some exemplary embodiments, oneor more device range rules may define a capture range of the body part325 for the corresponding PPE 320. For example, with half-maskrespirators, the device range rules may define a capture range as theuser face. In an exemplary embodiment of multiple PPE candidates, acapture range computing step may calculate a maximum facial area rangethat may accommodate the PPE and then correlate the range with each PPEto determine whether the respective PPE fits within the facial arearange.

Once the user body part is captured or retrieved, such as for examplefrom a database, the user body part may be modeled using a staticmodeling module 330. The static modeling module generates a 3D model ofthe user body part to be used by a dynamic modeling module 335. Thedynamic modeling module 335 communicates with an input module 340 forgenerating dynamic models of the user body part 325. In one exemplaryembodiment, the input module 340 communicates actual body movement 345to the dynamic modeling module 335 for generating a dynamic model usingactual movement from the user. In another exemplary embodiment, theinput module 340 communicates simulated movement 350 to the dynamicmodeling module 335 to generate a dynamic model based upon a simulatedbody part movement.

By using a dynamic model, a more realistic fit may be realized betweenthe user body part and the PPE, since a user generally undergoes somemovement while wearing the PPE. The dynamic model may then be comparedwith the PPE models 320 from the PPE database 315 by a comparator module355. The comparator module 355 may determine a fit level of the PPEmodel 320 with the dynamic model from the dynamic modeling module 335based on a variety of predetermined criteria or rules. For example, thecomparator module 355 may evaluate a size of the PPE model 320 with thedynamic model in an extreme position (e.g., open mouth) to determinewhether the PPE (e.g., respirator) will fit the user body part (e.g.,facial area) in the extreme position. The calculated results of thecomparator module 355 may be summarized for output and visualization.

In an exemplary embodiment, the comparator module 355 may fit the PPEmodel candidate 320 to the dynamic model set according to mapping rules.The comparator module 355 may then calculate the difference between thePPE model candidate 320 and dynamic model outputted from the dynamicmodeling module 335. According to a set of predetermined evaluationrules, a fit score of each PPE model 320 may be provided relative thedynamic model. Lastly, the comparator module 355 may output an optimalfit PPE 320 based on simulated comfort and fit. In an exemplaryembodiment, a respective fit of the PPE 320 may be visualized by colorcoding for user.

In an exemplary embodiment, the result from the comparator module 355may be outputted to a simulator module 360 for display to a user throughan output module. In an exemplary embodiment, the simulator module maygraphically overlay the 3D PPE model 320 upon a 3D representation of theuser body part 325 to illustrate to the user the PPE model 320 beingvirtually worn on the user body part 325. In some exemplary embodiments,a fit level, score, or color may accompany the graphical illustrationfor ease in interpreting the results.

In an exemplary embodiment, the output module 365 may comprise a displaymodule. In some exemplary embodiments, the output module 365 maycomprise a printed report. In some exemplary embodiments, the report mayprovide 3D visual representations of the PPE virtually worn by the user.In some exemplary embodiments, the report may provide a detailed list ofa fit level or score of each evaluated PPE with respect to a region ofinterest of the user. In some exemplary embodiments, the report mayprovide a color-coded graphical representation of a PPE virtual fit onthe user. In some exemplary embodiments the color-coded graphicalrepresentation may illustrate, through color-coding, different levels ofpressure as applied to the user by the PPE when virtually worn.

FIG. 4 depicts a flowchart of an exemplary process of a static modelingmodule. A static modeling module 400 may be used to generate anobjective static 3D model of a body part. The static model may be usedin dynamic modeling processes. In some exemplary embodiments, the PPEmay be compared directly to the static model if dynamic comparison isnot necessary. In an exemplary embodiment, a static model of a facialarea may be generated with the static modeling module 400. In someexemplary embodiments, the generated static model may be in 2D form.

When generating the static model, the module 400 first captures a regionof interest (ROI) point cloud of a user as in step 405. The ROI may bethe portion of the body that corresponds to the evaluated PPE. Forexample, when evaluating respirator fit, the ROI may be a facial area ofthe user. In an exemplary embodiment, the point cloud may include x, y,z coordinates assembled to form a 3D image of the respective body part.

A generic model may also generated as illustrated in step 410 togenerically match the body portion captured by the point cloud as instep 405. For example, if a facial area is the ROI, the generic modelmay be representative of a generic user face. In exemplary embodiments,the generic model may be retrieved from a database of generic models. Insome exemplary embodiments, a preliminary screening process may becompleted to find a generic model being close in shape to the capturedROI. Predetermined semantic information is attributed to the genericmodel as in step 415. The semantic information may be distinguishablebody feature points of the corresponding body part. For example, afacial area may include semantic information associated with the eyes,ears, mouth corners, and a nose tip. The semantic information may beattributed to the vertices of the generic model, for example. In anexemplary embodiment, a set of rules which define the semanticinformation may include MPEG4 format definition rules.

The point cloud of the ROI is then overlaid on the generic face model bya rigid method as in step 420. In an exemplary embodiment, the rigidmethod may include a rigid registration or alignment of vertices of theROI and vertices of the generic model. In an exemplary embodiment, thealigned vertices may correspond to proximally similar or equivalentlocations on the modeled body part. For example, the nose portion of thepoint cloud of the ROI may be aligned with nose portion of the genericmodel.

The module 400 then determines whether the vertices of the point cloudalign or match to an acceptable level or threshold as illustrated instep 425. For example, if the vertices of the point cloud do not exactlyalign as determined by a predetermined threshold, the vertices of thepoint cloud and the generic model are deemed not to align to anacceptable level. If the vertices do not align to an acceptable level,the vertices of the generic model may be deformed to fit the overlaidpoint cloud by a non-rigid registration method. In an exemplaryembodiment, a non-rigid registration method may include blending thenon-aligning vertices of the generic model with neighboring vertices. Inanother exemplary embodiment, certain vertices of the generic model maybe moved a predetermined allowable distance to reach an alignment withthe point cloud of the ROI.

Once alignment is reached with the vertices of the point cloud andvertices of the generic model, the semantic information of each vertexon the generic face model may be attributed to the point cloud. Forexample, the vertices of the point cloud may receive the semanticinformation and be stored within the properties of the point cloud suchthat each of the points in the point cloud may include identificationproperties corresponding to a location of the point in the point cloud.For example a point of the point cloud located at a positioncorresponding to a nose tip may include semantic information identifyingthe point as “nose tip”. The static model having the point cloud withsemantic information may then be outputted as in step 440. In anexemplary embodiment, the static model may be outputted to the dynamicmodeling module. In another exemplary embodiment, the static model maybe outputted to a comparator module. In yet another exemplaryembodiment, the static model may be outputted to a simulator module. Inan exemplary embodiment, the static model may be a 3D representation.

FIG. 5A depicts a flowchart of an exemplary dynamic modeling moduleusing actual body movement. A dynamic modeling module 500 uses actualbody movement to generate a dynamic model as described with reference toFIG. 3. In an exemplary embodiment, the dynamic model may be generatedin 3D form. In the depicted example, a user performs actual bodymovement as in step 505 and an image capture device captures the bodymovement as in step 510. In some exemplary embodiments, the bodymovement performed may correlate with body movement commonly performedwhile wearing associated PPE. For example, the user may speak a varietyof phrases when fitting a user with a respirator since it may be commonfor a user to speak or move their mouth while wearing the respirator.

Once a series of movements are captured, such as for example by video ora plurality of images, a key frame may be extracted from the capturedsequence as in step 515. The key frame may be an image reflecting aparticular user movement, for example. In an exemplary embodiment, thekey frame may simply be a generic reference or starting image. In anexemplary embodiment, the user may then manually mark feature points onthe selected, first key frame as in step 520. The feature points maycorrespond with distinguishable features on the body part captured. Forexample, a nose or mouth may be feature points for a captured facialarea. In some exemplary embodiments, the user manually selects thefeature points by visually selecting the feature points on a computerdisplay. In some exemplary embodiments, the user manually selects thefeature points by selecting body coordinates predetermined to beassociated with the respective feature point. In some exemplaryembodiments, the selection of the feature points may be automated via animage recognition software or device. In some exemplary embodiments, thefeature points may be appointed identifying information, such as forexample semantic information.

Once the feature points of the first key frame are identified andselected, a tracking method may be performed to identify and markfeature points on all key frames based on the selected feature points ofthe first key frame as in step 525. In an exemplary embodiment, thetracking method may track the feature points via proximity of similarvertices in neighboring key frames. In some exemplary embodiments, thetracking method may be automatically performed by the dynamic module500.

One of the key frames having feature points may then be selected and thefeature points corresponded to a static 3D model as in step 530. In anexemplary embodiment, the static 3D model may be generated according tothe detailed process exemplified in FIG. 4. The feature points may belinked to similarly located points from the point cloud of the staticmodel such that properties of the points from the point cloud of thestatic model may be transferred to the feature points of the key frame,for example.

In an exemplary embodiment, a morphed model may then be generated bymorphing the static model to a facial position of the key frame. Forexample, the point cloud of the static model may be altered to aproximal location of the feature points. If the key frame illustrates auser having an open mouth, the static model and associated point cloudmay be altered to reflect an open mouth morphed static model. In anexemplary embodiment, the morphable model may be generated by performingrigid and/or non-rigid registration methods with vertices or pointsbetween the key frame and the static model.

In step 540, the module 500 determines whether there are additional keyframes to analyze. If there are more key frames to analyze, then themodule cycles to the next key frame as in step 545 and returns to step530. If there are no more key frames to analyze, then a dynamic modelmay be outputted as in step 550. In an exemplary embodiment, the dynamicmodel may be outputted to a comparator module for comparing the PPEmodel with the dynamic model to determine whether the PPE model fits thedynamic model. In an exemplary embodiment, the dynamic model may beoutputted as a 3D model set for all captured key frames.

FIG. 5B depicts an exemplary graphical view of the dynamic modelingmodule of FIG. 5A. As illustrated, a key frame 555 having feature pointsmanually marked on the key frame as described with reference to step 520of FIG. 5A. The static model 560 may be generated by the static modelingmodule as described with reference to FIG. 4. As exemplified the pointcloud of the static model may be located along similar facial featuresas the feature points of the key frame. A morphable model 565 may begenerated by combining the key frame and the static model as describedwith reference to step 535 of FIG. 5A.

FIG. 6A depicts a flowchart of an exemplary dynamic modeling moduleusing simulated body movement. A dynamic modeling module 600 usessimulated body movement to generate a dynamic model as described withreference to FIG. 3. In an exemplary embodiment, the dynamic model maybe generated in 3D form. In an exemplary embodiment, an extreme movementset is defined as in step 605. The extreme movement set may be definedby a user in an exemplary embodiment. In other exemplary embodiments,the extreme movement set may be defined by the PPE manufacturer,regulatory agency, and/or employer. In an exemplary embodiment of arespirator, extreme movements may include raising the head, bowing thehead, speaking, and/or opening the mouth. In an exemplary embodiment,the extreme movement set may be defined according to actions a user maytypically undergo while wearing the respective PPE.

Once the extreme movement set is defined, a first extreme movement maybe selected from the set as in step 610. Feature points affected by theselected extreme movement are marked or identified on a static model.For example, if the extreme movement selected mimics an open mouth,feature points surrounding a mouth of the static model may be marked oridentified. In an exemplary embodiment, the feature points are linked tocorresponding proximal vertices. The static model may be generated by aprocess as exemplified with reference to FIG. 4, for example.

An influence zone of each feature point may also be defined on thestatic model as illustrated by step 620. In an exemplary embodiment, theinfluence zone may be a proximal area of each feature point that may beaffected by movement of the respective vertex. In an exemplaryembodiment, the feature points and/or feature point influence area maycorrespond to predetermined data points of an MPEG 4 standard. In anexemplary embodiment, the feature points may include semanticinformation.

In some exemplary embodiments, the user manually selects the featurepoints by selecting body coordinates predetermined to be associated withthe respective feature point. In some exemplary embodiments, theselection of the feature points may be automated via an imagerecognition software or device. In some exemplary embodiments, thefeature points may be appointed identifying information, such as forexample semantic information.

The maximum feature point position is also defined as in step 625. Themaximum feature point may correspond to a maximum distance and x, y, zcoordinate location of the feature point away from a normal or currentlocation of the feature point on the static model. The vertices andlinked feature points are then displaced to the maximum position asdefined by the extreme movement as in step 630 and a morphed model isformed as in step 635. Under a prior defined deformation functionaffect, neighbor related points on static model are displaced to a newposition. In an exemplary embodiment, the displacement position ofneighbor points can be calculated by:

D _(vertex) =D _(FP) *H(vertex,FP)

Where D_(vertex) may be the displacement position of neighbors, D_(FP)is displacement of feature points, H is the deformation function. Theinfluence zone of each feature point may also be blended or alteredaccording to linked feature point movement. In an exemplary embodiment,if a vertex is affected by more than one feature point, neighboringvertices may be blended by a weighted sum.

A deformation function may be defined as:

${H(f)} = \left\{ \begin{matrix}T & {{f} \leq \frac{1 - \beta}{2T}} \\{{\frac{T}{2}\left\lbrack {1 + {\cos \left( {\frac{\pi \; T}{\beta}\left\lbrack {{f} - \frac{1 - \beta}{2T}} \right\rbrack} \right)}} \right\rbrack},} & {\frac{1 - \beta}{2T} < {f} \leq \frac{1 + \beta}{2T}} \\0 & {otherwise}\end{matrix} \right.$

where T is the radius of the area that applies the deformation, and βwith the scope of (0, 1) is a parameter to adjust the deformationdegree; if 13 is close to 1, the deformation will be smooth, and if β isclose to 0, the deformation will be sharp.

In step 640, the module 600 determines whether there are additional keyframes to analyze. If there are more extreme movements to analyze, themodule cycles to the next extreme movement as in step 645 and returns tostep 610. If there are no more extreme movements to analyze, a dynamicmodel may be outputted as in step 650. In an exemplary embodiment, thedynamic model may be outputted to a comparator module for comparing thePPE model with the dynamic model to determine whether the PPE model fitsthe dynamic model. In an exemplary embodiment, the dynamic model may beoutputted as a 3D model set for all captured key frames.

FIG. 6B depicts a graphical view of the exemplary dynamic modelingmodule of FIG. 6A. The exemplary process includes a first definedextreme movement 655, such as for example an open mouth. A static model660 may be imported, such as for example the static model generated bythe static model module with reference to FIG. 4. The feature points 665and influence zones 670 corresponding to the extreme movement 655 aremarked on the static model 660. In an exemplary embodiment, the featurepoints may be located by correspondence with an MPEG 4 standard. Adeformation function 675 may be executed to morph the static model bydisplacing the feature points and influence zone according to themaximum position as defined by the extreme movement. Then, a morpheddynamic model 680 is outputted. The dynamic model may be generated in 3Dform. In an exemplary embodiment, the dynamic model has a body positioncorrelating to the defined extreme movement.

FIG. 7 depicts an overview of another exemplary PPE selection system. APPE selection system 700 may be used to select an optimal fit PPE for auser body part based on an internal space measured between the PPE andthe body part. In an exemplary embodiment, the internal space may bemeasured during a deformed state of the PPE. The PPE selection system700 includes a device type selection module 705 for receiving a commandfrom a user 710. In the depicted example, the device type selectionmodule 705 sends commands to a PPE database 715. The PPE database 715may include a variety of types of PPE 720, such as for example gloves,respirators, and helmets. In an exemplary embodiment, the device typeselection module 710 may relay a command 710 indicative of a particulartype of PPE 720, such as for example a facial respirator. In anexemplary embodiment, the command 710 may be indicative of a particularuser body part 725 to be matched with the PPE 720 from the PPE database715.

In some exemplary embodiments, the device type selection module 705 maydirect an image capture device (not shown) to capture a 2D or 3D imageof the selected body part 725. In some embodiments, the PPE 720 may bemodeled in a corresponding 3D shape. In some exemplary embodiments, oneor more device range rules may define a capture range of the body part725 for the corresponding PPE 720. For example, with half-maskrespirators, the device range rules may define a capture range as theuser face. In an exemplary embodiment of multiple PPE candidates 720, acapture range computing step may calculate a maximum facial area rangethat may accommodate the PPE 720 and then correlate the range with eachPPE 720 to determine whether the respective PPE 720 fits within thefacial area range.

Once the user body part 725 is captured or retrieved, such as forexample from a database, the user body part 725 may be modeled using acontact line module 730. The contact line module 730 determines acontact line of the edge of the PPE 720 on the body part 725 of theuser. For example, a respirator sealing edge may be defined as thecontact line since the sealing edge may be the primary portion of therespirator that makes contact with the user facial area. The contactline may be determined by capturing an image of the user wearing the PPE720 and not wearing the PPE 720, and then using a subtractive functionto find the contact line. In an exemplary embodiment, the contact linemay be found by capturing a 2D or 3D image of the user wearing and notwearing the PPE 720. In another exemplary embodiment, the contact linemay be determined using previously captured models of users and/or PPE720. For example, the previously captured models may be aligned using arigid or non-rigid registration method to calculate a contact line. Oncethe contact line is found or calculated, the portion of the body part725 confined by the contact line may be determined. For example, aportion of a face confined and within the contact line of a respiratormay include a portion of a nose and a mouth.

A deformation module 735 may then be used to deform the PPE 720. The PPE720 may be deformed according to a set of predetermined rules. Forexample, if a respirator is known to partially collapse inwards acertain percentage during wear, the PPE 720 model may be deformed anamount or distance equivalent to a calculated standard collapse of anin-use respirator. In another exemplary embodiment, the degree ofdeformation may be determined by a maximum flex permissible by theconstruction of the PPE 720. In an exemplary embodiment, a deformationof an inside surface or part of the PPE 720 may be determined orcalculated from a deformation of an outside surface or part of the PPE720. In another exemplary embodiment, a deformation of an outside partof the PPE 720 may be computed by comparing the outside part of the PPE720 to a deformation of the inside part of the PPE 720.

A comparator module 755 may determine a fit level of the deformed PPE720 model 720 with respect to the portion of the body part 725 internalor confined by the contact line. In comparison, an internal measurementmay be made between the internal surface of the PPE 720 and the portionof the body part 725 confined or internal to the contact line. Forexample, a distance between an inside surface of a respirator and aportion of a user face perpendicular to the inside surface may becalculated while the respirator is in the deformed state. In anexemplary embodiment, the internal measurement may be a distance betweenthe PPE 720 and the body part 725. In another exemplary embodiment, theinternal measurement may be an internal volume confined between theinside of the PPE 720 and the corresponding body part 725. In someexemplary embodiments, the internal measurement may be compared againsta predetermined threshold to determine whether the PPE 720 meetspredetermined fit criteria. For example, if the predetermined thresholdis not large enough, the PPE 720 may be disqualified from an acceptablefit category of PPE 720. The calculated results of the comparator module740 may be summarized for output and visualization.

In an exemplary embodiment, the internal measurement may use an implicitfunction to calculate a distance between the inside part of the PPE 720and the corresponding body part 725. A Gaussian smooth function may thenbe applied to the distance calculation, for example. In some exemplaryembodiments, a color-coded result of the internal measurement may beoutputted to a user.

In an exemplary embodiment, the comparator module 740 may fit the PPE720 model candidate 720 to the user body part 725 set according tomapping rules. According to a set of predetermined evaluation rules, afit score of each PPE 720 model 720 may be provided relative the userbody part 725. Lastly, the comparator module 740 may output an optimalfit PPE 720 based on simulated comfort and fit. In an exemplaryembodiment, a respective fit of the PPE 720 may be visualized by colorcoding for user.

In an exemplary embodiment, the result from the comparator module 740may be outputted to a simulator module 745 for display to a user throughan output module. In an exemplary embodiment, the simulator module maygraphically overlay the 3D PPE 720 model 720 upon a 3D representation ofthe user body part 725 to illustrate to the user the PPE 720 model 720being virtually worn on the user body part 725. In some exemplaryembodiments, a fit level, score, or color may accompany the graphicalillustration for ease in interpreting the results.

In an exemplary embodiment, the output module 750 may comprise a displaymodule. In some exemplary embodiments, the output module 750 maycomprise a printed report. In some exemplary embodiments, the report mayprovide 3D visual representations of the PPE 720 device virtually wornby the user. In some exemplary embodiments, the report may provide adetailed list of a fit level or score of each evaluated PPE 720 devicewith respect to a region of interest of the user. In some exemplaryembodiments, the report may provide a color-coded graphicalrepresentation of a PPE 720 device virtual fit on the user. In someexemplary embodiments the color-coded graphical representation mayillustrate, through color-coding, different levels of pressure asapplied to the user by the PPE 720 device when virtually worn.

FIG. 8 depicts a flowchart of another exemplary PPE selection system. Inthe exemplary system 800, an optimal fit PPE for a user body part basedon an internal space measured between the PPE and the body part. Thesystem provides a method of measuring an internal or hidden spacebetween a PPE and an associated body part to generate an objective fitof the PPE on the body part. For example, a point cloud of an outsidepart of a PPE may be captured during a deformed and non deformed stateto determine a position or shape of an inside part of a PPE. In thedepicted example, a point cloud may be captured of a region of interest(ROI) of a body part of the user as in step 805. For example, a pointcloud may be captured of a facial area of the user. A point cloud may becaptured of a PPE as in step 810, such as for example a respirator. Inan exemplary embodiment, a point cloud may be captured both of theoutside part (e.g., outside surface) and the inside part (e.g., insidesurface) of the PPE. A point cloud may also be captured of the ROI withthe PPE being worn as in step 815.

In an exemplary embodiment, the point cloud data may include x, y, zcoordinates which may be assembled to form a 3D image of the intendedPPE and/or user body part. In an exemplary embodiment, a point cloud mayinclude semantic information or other identifying feature points of theuser body part and/or PPE. In some exemplary embodiments, an imagecapture device may directly capture a 2D or 3D image of the selectedbody part ROI and/or PPE. In some exemplary embodiments, previouslycaptured 2D or 3D images of body part ROI and/or PPE may be used. In anexemplary embodiment, when there is not a 3D PPE model, captured pointcloud of people with and without device may be retrieved independentlyand compared to get placement information for the outside part of PPE.An estimate of placement and fit of the inside part of PPE may be made,for example.

A contact line may also be defined as in step 820. The contact line maybe the point or edge that the PPE makes contact with the user ROI, suchas for example a sealing edge of a respirator on a face of a user. Oncethe contact line is determined a portion of the ROI that is confined orwithin the contact line may be determined as will be described.

A deformation of the PPE may also be calculated, measured, or determinedas in step 825. For example, a deformation of an inside or outside partof the PPE may be calculated or measured based on a deformation of arespective outside or inside part of the PPE. In an exemplaryembodiment, a degree of deformation may be predetermined by amanufacturer. In another exemplary embodiment, a degree of deformationmay be determined by an employer based on common workplace practices. Inan exemplary embodiment, the inside or outside part of the PPE may beused to generate the deformed PPE structure, thus only one of the insideor the outside part of the PPE may be needed.

The internal space between the PPE and the portion of the ROI confinedby the contact line may then be measured as in step 830. In an exemplaryembodiment, the internal space may be determined based on aperpendicular distance between the PPE and the ROI. In another exemplaryembodiment, the internal space may be determined by a contained volumebetween the PPE and the ROI. In an exemplary embodiment, the internalspace may be measured while the PPE is in a deformed state.

A comparator module may determine whether a threshold has been met bythe measured internal space as in step 835. If a predetermined thresholdhas been met, then a positive recommendation may be outputted to a useras in step 840. In an exemplary embodiment, a 3D visual representationof the PPE on the ROI may be displayed to the user. In another exemplaryembodiment, the internal measurement may be displayed on the 3D visualrepresentation. If a predetermined threshold has not been met, then anegative recommendation may be outputted to a user as in step 845. Forexample, if the distance between an internal surface of a respirator andthe beneath facial area does not meet a predetermined length, then therespirator may fail a fit test.

FIG. 9A depicts a flowchart of another exemplary PPE selection system.In the exemplary system 900, an optimal fit PPE for a user body partbased on an internal space measured between the PPE and the body part.In the depicted example, the system 900 may compute a fit of the PPE onthe user body part based on previously captured point clouds of the PPEand/or body part. In an exemplary embodiment, point cloud data may becaptured of the body part ROI (e.g., face) and the PPE (e.g.,respirator) as in step 905. In some exemplary embodiments, point clouddata may be captured of the ROI with the PPE being worn.

If the PPE is not presently available, previously captured and storedpoint cloud data may be used to determine placement of the PPE on theROI. For example, point cloud data of the PPE may be overlaid upon pointcloud data of the ROI as in step 910. In an exemplary embodiment, thepoint cloud data may be aligned using a rigid-registration method. In anexemplary embodiment, the rigid-registration method aligns featurepoints of the PPE and ROI. In another exemplary embodiment, therigid-registration method aligns semantic information of the PPE andROI. In another exemplary embodiment, the rigid-registration methodaligns corresponding vertices of the PPE and ROI.

In an exemplary embodiment, once the PPE is placed on the ROI, a contactline of the PPE on the ROI may be obtained as in step 915. The contactline may be the point or edge that the PPE makes contact with the userROI, such as for example a sealing edge of a respirator on a face of auser.

In an exemplary embodiment, the contact line may be visibly orcomputationally defined and such that a center part of the contact linemay be determined. For example, the center part of the contact line maybe the center of a medial axis of the contact line. In an exemplaryembodiment, the medial axis may be vertically oriented and separate leftand right sides of the space confined by the contact line. A center partof an inside part of the PPE may also be computationally determined andthe center part of the PPE and the center part of the ROI are aligned.In an exemplary embodiment, rigid-registration methods may be used toobtain placement of the PPE on the ROI by aligning the center parts ofthe PPE and the ROI. Once the center parts of the ROI and PPE arealigned, corresponding points of the ROI and PPE may be determined andconfirmed such as for making internal measurements.

A deformation of the PPE may be calculated, measured, or determined asin step 930. For example, a deformation of an inside or outside part ofthe PPE may be calculated or measured based on a deformation of arespective outside or inside part of the PPE. In an exemplaryembodiment, a degree of deformation may be predetermined by amanufacturer. In another exemplary embodiment, a degree of deformationmay be determined by an employer based on common workplace practices. Inan exemplary embodiment, the inside or outside part of the PPE may beused to generate the deformed PPE structure, thus only one of the insideor the outside part of the PPE may be needed.

The internal space between the PPE and the portion of the ROI confinedby the contact line may then be measured as in step 935. In an exemplaryembodiment, the internal space may be determined based on aperpendicular distance between the PPE and the ROI. In another exemplaryembodiment, the internal space may be determined by a contained volumebetween the PPE and the ROI. In an exemplary embodiment, the internalspace may be measured while the PPE is in a deformed state.

A comparator module may determine whether a threshold has been met bythe measured internal space as in step 940. If a predetermined thresholdhas been met, then a positive recommendation may be outputted to a useras in step 945. In an exemplary embodiment, a 3D visual representationof the PPE on the ROI may be displayed to the user. In another exemplaryembodiment, the internal measurement may be displayed on the 3D visualrepresentation. If a predetermined threshold has not been met, then anegative recommendation may be outputted to a user as in step 950. Forexample, if the distance between an internal surface of a respirator andthe beneath facial area does not meet a predetermined length, then therespirator may fail a fit test.

FIG. 9B depicts an exemplary center part on a ROI as defined withreference to FIG. 9A. A region of interest ROI 955 may be a body partthat is to be protected, such as by a corresponding PPE. In an exemplaryembodiment, the ROI 955 may be a facial area of a user. The ROI 955 maybe illustrated in a 3D form to a user. In an exemplary embodiment, theROI 955 includes point cloud data used in the construction of the 3Dform and the fitting of the PPE.

In an exemplary embodiment, a contact line 960 may be defined on the ROI955, as previously defined with reference to step 915 of FIG. 9A. Thecontact line 960 may be peripheral edge of the PPE that makes contactwith the ROI 955, such as for example a sealing edge of a respirator. Inan exemplary embodiment, the contact line 960 may be computationallydetermined by comparing a user ROI while wearing and while not wearing aPPE. In another exemplary embodiment, the contact line 960 may bemanually drawn on the ROI by tracing a peripheral edge of the PPE wornon the ROI.

A center part 965 of the contact line 960 may also be defined, aspreviously defined with reference to step 920 of FIG. 9A. In anexemplary embodiment, the center part 965 includes a medial axis 970 andaxis center 975. The medial axis 970 may separate two-halves of the areaof the ROI 955 defined by the contact line 960. For example, the medialaxis 970 may separate left and right halves of the area of the ROI 955defined by the contact line 960. In an exemplary embodiment, the axiscenter 975 may be the lengthwise center of the medial axis 970.

In an exemplary embodiment, a PPE center part including a PPE medialaxis and PPE axis center are also defined on the PPE with reference to acontact edge (e.g., sealing edge of a respirator). The PPE medial axisand PPE axis center of the PPE are then aligned with the medial axis 970and axis center 975 of the ROI to determine a placement of the PPE onthe ROI, as previously defined with reference to step 925 of FIG. 9A.

FIG. 10 depicts a flowchart of an exemplary deformation process. Adeformation module 1000 may determine a deformation of an inside part ofPPE by correspondence with a deformation of an outside part of the PPEto obtain an internal measurement of the PPE and body part fordetermining whether the PPE fits the body part. The module 1000 firstcaptures point cloud data of the body part and PPE as in step 1005. Insome exemplary embodiments, the point cloud data may be captured earlierin the process and relayed to the deformation module 1000. The pointcloud sets are overlaid upon each other as in step 1010 to fit the PPEto the body part. In some exemplary embodiments, the fitting process maybe performed by other modules and the result relayed to the deformationmodule 1000.

The module 1000 may then obtain deformation parameters of the outsidepart of the PPE as in step 1015. In an exemplary embodiment, the outsidepart of the PPE may be an outside surface of the PPE with respect to thePPE being worn by a user. In an exemplary embodiment, the deformationparameters may be predetermined according to specific constructionproperties of the PPE. In another exemplary embodiment, the deformationparameters may be determined by functions, such as for example thedeformation function described with reference to FIG. 6A.

The PPE outside part may then be corresponded to the PPE inside part asin step 1020. For example, corresponding inside and outside part pointsmay be correlated based upon a nearest distance between inside pointsand outside mesh nodes. In another exemplary embodiment, inside pointsor vertices determined to be physically affected by specific outsidepoints or vertices are linked. For example, moving a point A on anoutside part may correspondingly move a point B on an inside part of thePPE, and thus point A may be linked to some degree to point B.

Once all necessary inside and outside part points of the PPE have beenlinked, the outside point deformation parameters previously defined instep 1015 are attributed to the respective inside points as in step1025. The PPE inside part may then be computationally deformed as instep 1030. In an exemplary embodiment, the PPE inside part may bedeformed according to the attributed deformation parameters linked tothe respective inside part in step 1025.

The internal space between the inside part of the PPE and the portion ofthe ROI confined by the contact line may then be measured as in step1035. In an exemplary embodiment, the internal space may be measuredwhile the inside part of the PPE is in the deformed state. In anexemplary embodiment, the internal space may be determined based on aperpendicular distance between the PPE and the ROI. In another exemplaryembodiment, the internal space may be determined by a contained volumebetween the PPE and the ROI.

A comparator module may determine whether a threshold has been met bythe measured internal space as in step 1040. If a predeterminedthreshold has been met a positive recommendation may be outputted to auser as in step 1045. In an exemplary embodiment, a 3D visualrepresentation of the PPE on the ROI may be displayed to the user. Inanother exemplary embodiment, the internal measurement may be displayedon the 3D visual representation. If a predetermined threshold has notbeen met, then a negative recommendation may be outputted to a user asin step 1050. For example, if the distance between an internal surfaceof a respirator and the beneath facial area does not meet apredetermined length, then the respirator may fail a fit test.

FIG. 11 depicts a graphical representation of an exemplary color-codeddisplay of a PPE fit. A display 1100 may be outputted to a user by anoutput module for providing a visual recommendation of a PPE fit. In anexemplary embodiment, the display 1100 may be outputted on a computerscreen. In another exemplary embodiment, the display 1100 may beoutputted in a printable format.

The display 1100 includes a representation of the evaluated user bodypart 1105, for example a facial area. In an exemplary embodiment, thebody part 1105 may be portrayed in 3D form. The body part 1105 may becolored according to pressure distribution as applied on the body part1105 by the PPE. In an exemplary embodiment, the PPE may be shown withthe body part 1105. In the depicted example, the display 1100 includes areference chart 1110 of the colors illustrated on the body part 1105 andvalues 1115 associated with each of the colors on the color chart 1110.The values 1115 may represent ranges of pressure distribution, forexample.

In an exemplary embodiment, a user may visually determine whether a PPEwould provide an acceptable fit by visualizing whether any areas uponthe body part 1105 are a certain color. For example, if an area of thebody part 1105 were colored red, a high degree of applied pressure maybe applied to the body part 1105 by the respective PPE. For example, arespirator may fit tightly against a face of a user in a certain area.In an exemplary embodiment, if a certain color were displayed on thebody part 1105 which would represent a threshold being exceeded, therespective PPE may be disqualified from further consideration withrespect to the specific user.

In another exemplary embodiment, shapes or symbols, rather than colorsmay be visually displayed on the body part 1105 to symbolize measuredcriteria. For example, a first shape may represent a first pressureapplied to the body part 1105 by the PPE and a second color mayrepresent a second pressure applied to the body part 1105 by the PPE. Inanother exemplary embodiment, a first color, shape, or pattern may beoverlaid upon the body part 1105 to represent a first distance that thePPE is from the body part when virtually worn, and a second color,shape, or pattern may be overlaid upon the body part 1105 to represent asecond distance that the PPE is from the body part when virtually worn.

FIG. 12 depicts a flowchart of an exemplary color-coded resultgenerator. A color-coded result generator 1200 may associated a set offit results with one or more colors to provide a user with a quickmethod of determining whether a respective PPE would fit and/or becomfortable. The fit results may be calculated and assigned to each ofthe points on the point cloud, such that each point in the point cloudof the ROI may have an associated fit result as illustrated in step1205. The result generator 1200 may calculate and assign the fit resultsor the fit results may be imported. In an exemplary embodiment, the fitresults each include an applied pressure upon the body part by the PPE.For example, the fit results may include a pressure applied to a facialarea by the respirator at each defined point or vertices.

The generator 1200 may correlate one or more colors to one or morepredetermined ranges as in steps 1210 and 1215. For example, a firstrange of applied pressure values may be assigned a first color, forexample a blue color. A second range of applied pressure values may beassigned a second color, for example a green color. The generatordetermines whether more colors are needed as in step 1220 and generatesadditional colors with assigned predetermined ranges as in step 1225. Inanother exemplary embodiment, a set of predetermined colors may beinitially assigned that include all possible ranges, such as for examplefrom −∞ to +∞.

The color ranges may then be corresponded to the fit results as in step1230. For example, a green color overlay on the human body part mayrepresent an optimal match and a red color overlay on the human bodypart may represent a non optimal match. In an exemplar embodiment, thecolors may represent how tight or loose PPE may be relative the humanbody part, such as for example red being shown for an area of the bodypart where the PPE product fits too tightly and green may be shown foran area of the body part where the PPE product fits too loosely.

The colors may then be overlaid on a body part ROI representation as instep 1235 and the result may be outputted to the user as in step 1240.In an exemplary embodiment, the body part ROI representation may be in3D form. An exemplary output is shown by display 1100 of FIG. 11.

Although various embodiments have been described with reference to theFigures, other embodiments are possible. For example, in someembodiments, the system and method for automatically selecting arespirator may comprise predictive software that may capture a facialimage and match the facial image to the closest form of a respiratormodel, type, and/or size. In an exemplary embodiment, the software mayuse a dynamic set of images and match the images to the flexibility of arespirator shape to predict an interface between the respirator modeland the facial model. For example, the software may predict whether theinterface between the respirator and the facial area will result inseparation thus permitting leakage or breach in the sealing surface.

In various embodiments, the image capture device may be a 3D digitalscanner, such as for example one or more Kinect devices manufactured byMicrosoft®. In some embodiments, the image capture device may be a stillcamera device. In some exemplary embodiments, the image capture devicemay be a video recorder device. In some exemplary embodiments, the imagecapture device may be a handheld unit. The image capture device may bewirelessly connected to a processing module for receiving a scannedimage from the image capture device and determining whether a scanned ormodeled PPE fits a scanned or modeled body part. In some exemplaryembodiments, the image capture device may be a low-cost item.

In various embodiments, apparatus and methods may involve a digitalimage of a facial area of a user in a variety of facial positions. Forexample, a first facial position may be a grin or smile. A second facialposition may be the user voicing specific letters and/or sounds. In anexemplary embodiment, software may digitize the facial shape of the userin each of the facial positions to create a flexible electronic file,for example. In an exemplary embodiment, software may also store fileshaving contours of respirators in both a static state and in a flexedstate for comparison to facial shape files. In an exemplary embodiment,the software may match up a negative cavity of the respirator model witha positive face form of the facial area model to determine a fit levelof a respirator or best fit respirator. In some exemplary embodiments,software may match the respirator and the facial area in both static anddynamic positions of the facial area and/or respirator to determinewhether a respirator will fit in a variety of facial positions and/orflexed positions of the respirator.

In an exemplary embodiment, an administrator may oversee a matchingprocess of the respirator and a specific facial area. For example, anadministrator at a workplace may oversee the matching process for eachnew employee. In some examples, each employee may undergo a matchingprocess, such as for example via a pay per use web link. In someexemplary embodiments, a kiosk or vending machine may include softwarefunctionality to perform a matching process between one or morerespirators and a specific facial shape. For example, a user may scan auser facial shape at a kiosk, and the kiosk may geometrically comparethe facial shape of the user to a plurality of respirator modelsavailable for dispensing to find a respirator that most closely matchesthe facial shape of the user. Upon finding an optimal or best fitrespirator, the kiosk may dispense the respective respirator or providedirection to the user on where the respirator may be available forpickup and/or purchase, for example.

In accordance with another embodiment, a population data gathering andstorage system may be made available via scanning facial areas of users.In some examples, the facial shapes gathered and stored via the matchingprocess may be used by respirator manufacturers to improve a respiratordesign such that newly manufactured respirators more closely match acommon facial shape of persons commonly wearing the respirators. In someexamples, the facial shapes gathered and stored via the matching processmay be used by employers to provide insight on which respirators tostock in greater or less numbers. In some exemplary embodiments, acaptured point cloud of a PPE and/or a user body part may be re-used inother PPE design.

In accordance with another embodiment, a variety of body parts may bescanned and captured for being matched with respective clothing orgarments. For example, a hand of a user may be scanned and stored as adata set such that a variety of glove models, types, and/or sizes may becompared against the hand of the user to find an optimal or best fitglove. In another exemplary embodiment, a head of a user may be scannedand stored as a data set such that a variety of helmet models, types,and/or sizes may be compared against the head of the user to find anoptimal or best fit helmet.

In accordance with an exemplary embodiment, a system and method forselecting a respirator may include a body modeling module for capturingan image(s) of a body part (e.g., facial area) of a user. In anexemplary embodiment, the image(s) may be used to generate a 3D model ofthe body part.

In some embodiments, the system and method for selecting a respiratormay include one or more product databases of PPE 3D models. For example,each product database may include PPE to be worn on a specific bodypart. In an exemplary embodiment, a respirator database may beassociated with facial areas, a glove database may be associated withhands, and a helmet database may be associated with heads. In someexemplary embodiments, the material properties of each specific PPE mayalso be stored with the specific PPE model.

In some embodiments, the system and method for selecting a respiratormay include a rule library illustrating a method of mapping 3D PPEmodels to a 3D human body part. In an exemplary embodiment, a rulelibrary may include three types of rules, such as for exampleassociation rules, mapping rules, and evaluation rules. For example,association rules may define which related PPE 3D models from theproduct database are associated to a target body part. For example,respiratory products from product database may be associated to facemodels, and footwear products from product databases may be associatedto foot models. In an exemplary embodiment, mapping rules may define howthe product model will be mounted to the body model, such as for exampleby mapping directions, forces, and/or deformations according to amaterial property. In an exemplary embodiment, evaluation rules maydefine how well the PPE fits the body part in accordance with a mappingresult. For example, via dimensional comparison, a body dimension may becompared to a related product dimension range or pressure distributionduring and after the product is mapped to the body part.

In some embodiments, the system and method for selecting a respiratormay include a 3D geometry matching module. In an exemplary embodiment,the matching module may calculate all differences between the 3D PPEmodels and the 3D human body model. The geometry matching module mayselect a PPE part according to association rules, determine thedifference with the mapping rules, summarize the difference according tothe evaluation rules, and then propose a product model and/or size whichmay optimally fit a user. In an exemplary embodiment, a top three or topfive best fitting products may be provided to the user.

In some embodiments, the system and method for selecting a respiratormay include a simulator module. In an exemplary embodiment, a simulatormodule may visualize to a user how well the PPE model fits on the bodypart model. In some exemplary embodiments, the simulator may display thehuman body part and PPE product in 3D representations. In some exemplaryembodiments, color coding may be used to illustrate how well the PPEfits a human body part. For example, a green color overlay on the humanbody part model may represent an optimal match and a red color overlayon the human body part model may represent a non optimal match. In someexamples, the colors may represent how tight or loose the PPE may berelative the human body part, such as for example red being shown on anarea of the body part model where the PPE fits too tightly and greenshown on an area of the body part model where the PPE fits too loosely.

In accordance with an exemplary embodiment, the PPE selection system mayoutput a comfort level based on a predetermined measurement scale, wherethe comfort level may reference a relative comfort of a PPE virtuallyworn by a user. In some embodiments, a comfort level may be determinedby the amount of internal space measured between an inside part of a PPEand a corresponding body part. In some exemplary embodiments, a comfortlevel may be determined by a degree of permissible movement by arespective body part while a PPE is worn. For example, a comfort levelmay be determined for a respirator by determining whether the respiratormaintains a seal with a facial area while the mouth of the user is beingopened. In accordance with an exemplary embodiment, a user feeling maybe determined by an objective comfort evaluation based on quantitativemeasurement. For example, a module may calculate a numeric pressurelevel upon the facial model as applied by the respirator model andcompare the calculated pressure level with a set of predeterminedpressure ranges each associated with a specific comfort level.

A number of implementations have been described. Nevertheless, it willbe understood that various modification may be made. For example,advantageous results may be achieved if the steps of the disclosedtechniques were performed in a different sequence, or if components ofthe disclosed systems were combined in a different manner, or if thecomponents were supplemented with other components. Accordingly, otherimplementations are within the scope of the following claims.

1. A method of predictively fitting a respirator to a specific user, themethod comprising: retrieving respirator shape data representative of ashape of a selected respirator; capturing facial shape datarepresentative of a facial shape of a selected facial area; overlayingsaid respirator shape data upon said facial shape data to obtaindeformation parameters for said respirator; computationally deformingthe respirator shape data according to the obtained deformationparameters; determining an internal dimension between an internalsurface of said respirator and a portion of said facial area behind saidrespirator; and generating a fit recommendation for display to a userbased upon said internal dimension between said respirator and saidfacial area.
 2. The method of claim 1, further comprising a step ofdetermining whether said internal dimension exceeds a minimum threshold,wherein said fit recommendation is based on said minimum threshold. 3.The method of claim 1, wherein said internal dimension comprises adistance between said respirator and said facial area.
 4. The method ofclaim 1, wherein said internal dimension comprises an internal volumebetween said respirator and said facial area.
 5. The method of claim 1,further comprising a step of computing an internal shape of saidrespirator based upon an external shape of said respirator.
 6. Themethod of claim 1, further comprising a step of overlaying saidrespirator upon said facial area in a deformed state of said respirator.7. The method of claim 1, further comprising a step of overlaying saidrespirator upon said facial area in a non-deformed state of saidrespirator.
 8. The method of claim 1, further comprising a step ofgenerating a color-coded display of said facial area indicative of a fitof said respirator on said facial area.
 9. The method of claim 1,further comprising a step of storing a plurality of point cloud datasets each representative of a shape and size of a specific respirator ina respirator database.
 10. A method of predictively fitting a respiratorto a specific user, the method comprising: retrieving respirator shapedata representative of a shape of a selected respirator; capturingfacial shape data representative of a facial shape of a facial area of auser, wherein said facial shape data is captured with and without saidrespirator being worn; overlaying said respirator shape data upon saidfacial shape data to obtain deformation parameters for said respirator;computationally deforming the respirator shape data according to theobtained deformation parameters; determining a contact line of saidrespirator on said facial area for location of a portion of said facialarea behind said respirator; determining an internal dimension betweenan internal surface of said respirator and said portion of said facialarea behind said respirator; and generating a fit recommendation fordisplay to a user based upon said internal dimension between saidrespirator and said facial area.
 11. The method of claim 10, furthercomprising a step of determining whether said internal dimension exceedsa minimum threshold, wherein said fit recommendation is based on saidminimum threshold.
 12. The method of claim 10, wherein said internaldimension comprises a distance between said respirator and said facialarea.
 13. The method of claim 10, wherein said internal dimensioncomprises an internal volume between said respirator and said facialarea.
 14. The method of claim 10, further comprising a step of computingan internal shape of said respirator based upon an external shape ofsaid respirator.
 15. The method of claim 10, further comprising a stepof modifying said respirator shape data to a deformed state.
 16. Themethod of claim 10, further comprising a step of overlaying saidrespirator upon said facial area in a non-deformed state of saidrespirator.
 17. The method of claim 10, further comprising a step ofgenerating a color-coded display of said facial area indicative of a fitof said respirator on said facial area.
 18. A system for predictivelyfitting a respirator to a specific user, the system comprising: aprocessor module for defining facial shape data corresponding to afacial shape of a facial area and respirator shape data corresponding toa respirator shape and size; means for determining an internal dimensionbetween said respirator and said facial area by overlaying saidrespirator shape data upon said facial shape data to obtain deformationparameters for said respirator and computationally deforming therespirator shape data according to the obtained deformation parameters;and a recommendation module for display to a user based upon saidinternal dimension.
 19. The system of claim 18, wherein said processingmodule computes an internal shape of said respirator based upon anexternal shape of said respirator.
 20. The system of claim 18, whereinsaid display module comprises a color-coded facial area display.